Paper Citation Record · LEDGER

Orthogonalising gradients to speed up neural network optimisation

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2202.07052.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2202.07052 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:28:29.172170Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T01:56:27.831756Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 33285297-0730-4f02-98af-685372b2a3d2 · inbound

SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training cites this paper.

SWAN: SGD with Normalization and Whitening Enables Stateless LLM Training Orthogonalising gradients to speed up neural network optimisation

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T13:28:29.172170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6292aa8e-5ebf-486c-a8e2-929268356084 · inbound

Low-rank Orthogonalization for Large-scale Matrix Optimization with Applications to Foundation Model Training cites this paper.

Low-rank Orthogonalization for Large-scale Matrix Optimization with Applications to Foundation Model Training Orthogonalising gradients to speed up neural network optimisation

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:51:33.984117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 209a3436-6425-4966-bdef-14f9a80ad52c · inbound

Pion: A Spectrum-Preserving Optimizer via Orthogonal Equivalence Transformation cites this paper.

Pion: A Spectrum-Preserving Optimizer via Orthogonal Equivalence Transformation Orthogonalising gradients to speed up neural network optimisation

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-05-13T04:57:17.662541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7e7da1cb-6edc-49ab-b841-e7fe21312bdf · inbound

Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers cites this paper.

Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers Orthogonalising gradients to speed up neural network optimisation

Reference 148

Resolution: verified exact; arxiv_id, observed 2026-05-20T09:38:11.132991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4895560e-23d2-47e5-9adc-246c763b1bc4 · inbound

Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers cites this paper.

Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers Orthogonalising gradients to speed up neural network optimisation

Reference 150

Resolution: verified exact; arxiv_id, observed 2026-06-30T18:45:00.364512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5114474a-342d-44ce-ab8f-6bbb1af561a3 · inbound

Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer cites this paper.

Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer Orthogonalising gradients to speed up neural network optimisation

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-25T02:56:33.517409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a839502a-3e26-42cd-92b1-5fc59dcbd6a1 · inbound

Denoise First, Orthogonalize Later: Understanding Momentum in Muon via Spectral Filtering cites this paper.

Denoise First, Orthogonalize Later: Understanding Momentum in Muon via Spectral Filtering Orthogonalising gradients to speed up neural network optimisation

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:56:27.833537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4cd44b55-1ac0-4950-b20e-94405a2c39d0 · inbound

Reassessing Muon for Matrix Factorization cites this paper.

Reassessing Muon for Matrix Factorization Orthogonalising gradients to speed up neural network optimisation

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-02T05:49:25.669442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 255b4c59-575c-4ab9-801a-983a749a9932 · inbound

Reassessing Muon for Matrix Factorization cites this paper.

Reassessing Muon for Matrix Factorization Orthogonalising gradients to speed up neural network optimisation

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-04T04:23:01.225267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.